I have not used Latent Gold but I can talk in general about the use of
mixture models in clustering. The statement "based on means and not on
probabilities" seems wrong.
All mixture models are based on viewing the data as a sample from a
finite mixture of probability distributions, each with an assumed
parametric form. When the mixture components are well=separated in the
data the clustering obtained is reasonably robust to the assumed form.
When the components overlap substantially there can be identifiability
problems and the form of the assumed distributions becomes more
influential on the solution.
Traditional statistical tests based on asymptotics are not of much
relevance because the number of parameters for these models is usually
large in comparison to the number of observations. Resampling may be of
use though, not so much for obtaining confidence regions for component
model parameters and mixture proportions as for see whether the main
features of a maximum likelihood solution are reproduced in resamples.
John Ubersax's Latent Class Pages
http://ourworld.compuserve.com/homepages/jsuebersax/
are a good resource for mixture models, although most attention is given
to discrete variables.
David Dowe's mixture modelling page
http://www.csse.monash.edu.au/~dld/cluster.html
is a useful resource and collection of links.
Murray Jorgensen
SUBSCRIBE CLASS-L Anonymous" wrote:
Hello
Does anyone know why p-values and chi-squared statistics are not available in
Latent Gold summary output for models using continuous variables and what is
the statistical explanation behind it?
Also, how reliable is the classification with continuous variables in latent gold
given the fact that it is based - from my understanding - on means and not on
probabilities?
Many thanks
Anca
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Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: [EMAIL PROTECTED] Fax 7 838 4155
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